Research Projects

Accurate and timely monitoring of crops is crucial for improving farming practices and increasing crop yield for sustainable food security. The literature reveals the limitations of previous studies and suggested that fully polarimetric SAR data can provide better information on crop scattering mechanism, leading to more accurate crop monitoring at high spatial and temporal resolutions. The previous algorithms developed for crop monitoring using radar technology have limitations in characterizing the different order of scattering from the crops, which vary across different crop types and growth stages. Moreover, the high levels of heterogeneity in farming practices in India make it challenging to apply polarimetry for crop monitoring. The absence of dedicated algorithms for full polarimetric based radar vegetation index algorithm for accurate monitoring and time-series mapping of crop types may result in less accurate monitoring of crops and potentially impact national agricultural programs such as SARAL and National Crop Health Mission. To address these limitations, further research is highly needed to develop more advanced SAR based algorithms that can account for the varying scattering mechanisms across different crops and growth stages, and to account for the high levels of heterogeneity in farming practices in India. These efforts could lead to more accurate and effective monitoring of crops and support national agricultural programs. The proposed project is focused on developing and utilizing a new Radar Vegetation Index (RVI) using fully polarized Synthetic Aperture Radar (SAR) data for crop monitoring. Compared to conventional SAR data, fully polarimetric SAR data can provide not only the geometric information and backscattering information but also the polarized information of the crops by microwaves with full polarimetric states, significantly improving the radar capability in scattering mechanism understanding. The polarimetric intensity, phase processing, and polarimetric decomposition will be utilized to fully exploit the polarimetric SAR data to identify the polarimetric parameters most closely related to vegetation parameters, which could potentially be used to accurately estimate crop growth variables. The project aims to address the challenges posed by the heterogeneity of the field and make crop monitoring more efficient and accurate. In conclusion, the potential benefits of developing novel algorithms based on full polarimetric SAR theory for crop monitoring are numerous, and the outcomes of such research could have a significant impact on various stakeholders. Developing novel algorithms based on polarimetry theory can address the limitations of previous studies and provide a powerful tool for crop monitoring in heterogeneous field conditions. The outcomes of this research could benefit various stakeholders, including farmers, the agricultural sector, the Indian government, industry, and academia.